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$24K GPU Matches $1 Million CPU Grid In Risk Performance

HSBC, which has been running its credit value adjustment (CVA) for measuring counterparty risk exposure on a grid, is experimenting with using GPUs to reduce processing time.

The Quantitative Risk and Valuation Group (QRVG) found it could accelerate processing times by 30x with two NVIDIA Tesla cards, said Eurico Covas, head of QRVG development at HSBC in London. Under the Basel III framework and other regulations, CVA analysis must be carried out regularly over a bank’s entire portfolio to compute its risk exposure and, consequently, its regulatory capital requirements.

QRVG turned to Xcelerit to drive its prototype GPUs without going through the complex programming that GPUs usually require. After a few days of learning how to work with Xcelerit, a single quant developer, Johary Ramanoelina, was able to rewrite a very small but highly used subset of the bank’s in-house developed libraries with the powerful data-flow technique used by the Xcelerit software development kit (SDK) to tackle the task of speeding up the existing code base.

The effects of the code transformation were dramatic. In one example of this proof of concept prototype, a set of 100,000 simple swap instruments was priced for a set of 1,000 Monte-Carlo scenarios at 26 time steps. This adds up to 2.6 billion price calculations in total. An average is taken over all scenarios to get the expected exposure at each one of the time steps. Speedups of 43x have been achieved on an NVIDIA Tesla K20 GPU, compared to an equivalent multi-core CPU implementation on an Intel Xeon E5450 CPU using all 4 physical cores. When run on a system with 2 GPUs, this performance scales almost linearly, achieving a speedup of nearly 78x.

“GPUs are very good in performance per dollar and per watt, but when it comes to programming they are very difficult, said Hicham Lahlou, Xcelerit CEO and co-founder. Using CUDA from NVIDIA for programming is a blocker for banks because the source code is tied to the device. Large banks have a lot of legacy code they don’t want to rewrite from scratch, he added.

NVIDIA logo (Photo credit: Wikipedia)

“Our answer at Xcelerit is to come out with a solution to start with a bank’s existing code. With Xcelerit, a quant develper could make minor modifications and run it on GPUs and multi-core CPUs with one single source code. We have lowered the barrier to GPU programming quite a lot. While we generate CUDA at the back end, we provide a front end layer that is easily understood by quants.”

Xcelerit can be used for complex derivatives pricing and in various areas of risk, Lahlou said.

“CVA and credit risk are very attractive at the moment because regulators are pushing banks to do it, while in other applications banks might just want to run them faster, which is not necessarily a big enough reason to deploy GPUs.”

A key to Xcelerit is that quant developers can work with it directly, without involving IT, said Covas.

“Today banks often have walls between quants who work on the models and business and IT and senior executives. Often nobody talks to each other, sometimes even in the same team. The successful companies understand that you need tools so quants can do the programming themselves cutting out the IT side. They may not know CUDA, but they know C++; as long as you have the right tools you can shift tasks from IT to the quants themselves.”

In addition to a 30x increase in performance using two NVIDIA Tesla cards compared to a grid, the GPU approach is a lot cheaper. A full development set (DEV/UAT/PROD/BACKUP) of GPUs will cost about $22,000 compared to $1 million for a 600-CPU grid. Production results may not be as impressive as the prototypes because they will have more sophisticated detailed models rather than a skeleton swap pricer, but it still looks a lot cheaper, Covas said.

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